ML Infrastructure Engineer
Salary not listed • Remote • Amsterdam, Netherlands; Remote - Europe; Remote - United States • Full-time
Posted 5mo ago
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Job Location
Amsterdam, Netherlands; Remote - Europe; Remote - United States
Tech Stack
Remote Work Policy
Fully remote
Employment Type
Full-time
Categories
AI Infrastructure Engineer
About the job
Nebius is building a full-stack AI cloud platform to support developers and enterprises from data and model training through to production deployment. We are seeking a highly skilled ML/AI Engineer to lead and support benchmarking of GPU platforms for machine learning and AI workloads. You will play a critical role in evaluating the performance of GPU-based hardware for various deep learning and AI frameworks, enabling data-driven decisions for platform optimisation and next-generation hardware development. This role involves working closely with hardware and development teams to profile and analyse GPU performance, evaluate and compare GPU performance across different platforms, and debug and optimise ML workloads to run efficiently on GPU hardware.
Responsibilities
- Profile and analyse GPU performance at the system and kernel level.
- Evaluate and compare GPU performance across different platforms, architectures, and software stacks.
- Debug and optimise ML workloads for GPU hardware efficiency.
- Perform acceptance testing for new GPU clusters.
- Conduct experiments across diverse GPU system configurations to assess performance and scalability.
- Develop tools and dashboards to visualise performance metrics and bottlenecks.
- Contribute to internal tooling, frameworks, and best practices.
Requirements
- Profound understanding of theoretical foundations of machine learning.
- Deep understanding of performance aspects of large neural network training and inference.
- Deep experience with modern deep learning frameworks (PyTorch, JAX, Megatron-LM, Tensort-LLM).
- Good understanding of the GPU stack: CUDA, NCCL, drivers, and relevant libraries.
- Familiarity with containerized environments (e.g., Docker, Kubernetes).
- Strong communication skills and ability to work independently.
Benefits
- Competitive compensation
- Career growth and learning opportunities
- Flexibility and ownership
- Collaborative and innovative culture
- Opportunity to work on impactful AI projects
- International environment and talented teams